Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale.

Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (L_nir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R² = 0.72, RMSE = 7.52 g/m²), outperforming conventional VIs-based (R² < 0.39, RMSE > 11.13 g/m²) estimations. The study further investigated the results of fAPAR_green-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn < 0.2) and higher diversity conditions (DMn > 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating L_nir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring.

Background/Objectives: Seasonal reproduction in mammals is primarily regulated by the hypothalamic–pituitary–ovarian (HPO) axis, yet its molecular mechanisms in subterranean rodents living in light-restricted environments remain poorly understood. This study aimed to characterize the transcriptional regulation of the HPO axis during seasonal estrus in the Manchurian zokor (Myospalax psilurus, M. psilurus), a fossorial rodent exhibiting distinct breeding cycles despite perpetual darkness. Methods: Hypothalamic, pituitary, and ovarian tissues were collected from female zokors during estrus and anestrus (n = 5 per group). RNA sequencing was performed, followed by de novo transcriptome assembly and bioinformatic analyses. Differentially expressed genes (DEGs) were identified using edgeR, and functional enrichment was assessed via GO and KEGG analyses. Key DEGs were validated by RT-qPCR. Results: A total of 513, 292, and 138 DEGs were identified in the hypothalamus, pituitary, and ovary, respectively. GO analysis highlighted enrichment in G-protein-coupled receptor signaling, oxidation–reduction processes, and calcium ion binding. KEGG pathway analysis revealed significant enrichment of the neuroactive ligand–receptor interaction pathway across all three tissues. Key candidate genes included Trh and Mc3r in the hypothalamus, Pitx2 and NR4A2 in the pituitary, and PTGER2 and Sphk1 in the ovary. Conclusions: This study provides the first comprehensive transcriptomic profile of the HPO axis in Manchurian zokors during seasonal estrus. The neuroactive ligand–receptor interaction pathway appears central to reproductive regulation, and several tissue-specific genes were identified as potential regulators of seasonal breeding. These findings enhance our understanding of reproductive adaptation in subterranean mammals and offer a foundation for further functional studies.